Top-Down Statistical Estimation on a Database,

Abstract

The size of data sets subjected to statistical analysis is increasing as computer technology develops. Quick estimates of statistics rather than exact values are becoming increasingly important to analysts. The author proposes a new technique for estimating statistics on a database, a top-down alternative to the bottom-up method of sampling. This approach precomputes a set of general-purpose statistics on the database, a database abstract, and then uses a large set of inference rules to make bounded estimates of other, arbitrary statistics requested by users. The inference rules form a new example of an artificial-intelligence expert system. There are several important advantages of this approach over sampling methods. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1984
Accession Number
ADA138318

Entities

People

  • N. C. Rowe

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • C4I
  • Ground and Sea Platforms

DTIC Thesaurus Topics

  • Abstracts
  • Artificial Intelligence
  • Computer Science
  • Computers
  • Data Analysis
  • Data Science
  • Databases
  • Expert Systems
  • Information Science
  • Information Systems
  • Language
  • Mathematics
  • Optimization
  • Statistical Analysis
  • Statistical Estimation
  • Statistical Samples
  • Statistics

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Database Systems and Applications
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference